April 2024 arXiv papers — page 97

Haixia Han, Tingyun Li, Shisong Chen, Jie Shi

Large Language Models (LLMs) have exhibited remarkable performance across various downstream tasks, but they may generate inaccurate or false information with a confident tone. One of the possible solutions is to empower the LLM confidence expression capability, in which the confidence expressed can be well-aligned with the true probability of the generated

Alexey Kornaev, Elena Kornaeva, Oleg Ivanov, Ilya Pershin

One of the ways to make artificial intelligence more natural is to give it some room for doubt. Two main questions should be resolved in that way. First, how to train a model to estimate uncertainties of its own predictions? And then, what to do with the uncertain predictions if they appear? First, we proposed an uncertainty-aware negative log-likelihood los

Maria Koutsoupidou, Dimitrios C. Tzarouchis, Dionysios Rompolas, Ioannis Sotiriou

Wearable, implantable, and ingestible antennas are continuously evolving in biomedical applications, as they are crucial components in devices used for monitoring and controlling physiological parameters. This work presents an experimentally validated wearable pad which can improve transmission of electromagnetic waves into the human body. This metamaterial-

Runyi Li, Xuhan Sheng, Weiqi Li, Jian Zhang

Omnidirectional images (ODIs) are commonly used in real-world visual tasks, and high-resolution ODIs help improve the performance of related visual tasks. Most existing super-resolution methods for ODIs use end-to-end learning strategies, resulting in inferior realness of generated images and a lack of effective out-of-domain generalization capabilities in t

Chengyang Gu, Yuxin Pan, Ruohong Liu, Yize Chen

In the context of charging electric vehicles (EVs), the price-based demand response (PBDR) is becoming increasingly significant for charging load management. Such response usually encourages cost-sensitive customers to adjust their energy demand in response to changes in price for financial incentives. Thus, to model and optimize EV charging, it is important

Hassam Khan Wazir, Zaid Waghoo, Vikram Kapila

Several therapy routines require deep breathing exercises as a key component and patients undergoing such therapies must perform these exercises regularly. Assessing the outcome of a therapy and tailoring its course necessitates monitoring a patient's compliance with the therapy. While therapy compliance monitoring is routine in a clinical environment, it is

Saheb Soroushfar, Hoda Farahani, Sudhaker Upadhyay

We extend the study of corrected thermodynamics for the 3D black holes conformally coupled to scalar field up to non-perturbative level. We calculate the exponential correction to entropy arises due to the microstate counting for quantum states on the boundary. This exponential correction in entropy attributes to the other thermodynamical quantities also. We

Woomin Song, Seunghyuk Oh, Sangwoo Mo, Jaehyung Kim

Large language models (LLMs) have shown remarkable performance in various natural language processing tasks. However, a primary constraint they face is the context limit, i.e., the maximum number of tokens they can process. Previous works have explored architectural changes and modifications in positional encoding to relax the constraint, but they often requ

Steve Andreas Immanuel, Hagai Raja Sinulingga

Few-shot segmentation is a task to segment objects or regions of novel classes within an image given only a few annotated examples. In the generalized setting, the task extends to segment both the base and the novel classes. The main challenge is how to train the model such that the addition of novel classes does not hurt the base classes performance, also k

Hengyuan Zhang, Yanru Wu, Dawei Li, Sak Yang

Aligned Large Language Models (LLMs) showcase remarkable versatility, capable of handling diverse real-world tasks. Meanwhile, aligned LLMs are also expected to exhibit speciality, excelling in specific applications. However, fine-tuning with extra data, a common practice to gain speciality, often leads to catastrophic forgetting (CF) of previously acquired

Nandigramam Sai Harshit, Nilesh Kumar Sahu, Haroon R. Lone

Depression is one of the most common mental disorders affecting an individual's personal and professional life. In this work, we investigated the possibility of utilizing social media posts to identify depression in individuals. To achieve this goal, we conducted a preliminary study where we extracted and analyzed the top Reddit posts made in 2022 from depre

Avinash Anand, Raj Jaiswal, Pijush Bhuyan, Mohit Gupta

The automatic recognition of tabular data in document images presents a significant challenge due to the diverse range of table styles and complex structures. Tables offer valuable content representation, enhancing the predictive capabilities of various systems such as search engines and Knowledge Graphs. Addressing the two main problems, namely table detect

Kaibo Liu, Zhenpeng Chen, Yiyang Liu, Jie M. Zhang

Detecting tricky bugs in plausible programs, those that pass existing test suites yet still contain bugs, remains a significant challenge in software testing. To address this problem, we propose TrickCatcher, an LLM-powered approach to generating test cases for uncovering bugs in plausible programs. TrickCatcher operates in three stages: First, it uses an LL

Nastaran Khazali, Gabriel Malgaresi, Ludmila Kuzmina, Yuri Osipov

Modelling of colloidal and nano-suspension transport in porous media has garnered significant attention due to the prevalence of these processes in many engineering applications. A number of experimental studies have reported retention profiles after coreflooding that are hyper-exponential, a feature that the traditional models for deep bed filtration are un

Qi Peng, Shuichiro Yokoyama, Kiyotomo Ichiki

A modification to the vis-viva equation that accounts for general relativistic effects is introduced to enhance the accuracy of predictions of orbital motion and precession. The updated equation reduces to the traditional vis-viva equation under Newtonian conditions and is a more accurate tool for astrodynamics than the traditional equation. Preliminary simu

Zach Evans, Julian D. Parker, CJ Carr, Zack Zukowski

Audio-based generative models for music have seen great strides recently, but so far have not managed to produce full-length music tracks with coherent musical structure from text prompts. We show that by training a generative model on long temporal contexts it is possible to produce long-form music of up to 4m45s. Our model consists of a diffusion-transform

E. Harikumar, K. V. Shajesh, Suman Kumar Panja

We investigate the response of Casimir energies to fluctuations in a scalar field in a weak gravitational field in the $\kappa$-deformed space-time. We model the Casimir plates in a gravitational field by $\kappa$-deformed Rindler coordinates and calculate the Casimir energy using the $\kappa$-deformed scalar field. We show that the Casimir energy accelerate

Shintaro Tamai, Masayuki Numao, Ken-ichi Fukui

Recently, growing health awareness, novel methods allow individuals to monitor sleep at home. Utilizing sleep sounds offers advantages over conventional methods like smartwatches, being non-intrusive, and capable of detecting various physiological activities. This study aims to construct a machine learning-based sleep assessment model providing evidence-base

Shujing Pan, Yong Wei

In this paper, we consider the anisotropic $\alpha$-Gauss curvature flow for complete noncompact convex hypersurfaces in the Euclidean space with the anisotropy determined by a smooth closed uniformly convex Wulff shape. We show that for all positive power $\alpha>0$, if the initial hypersurface is complete noncompact and locally uniformly convex, then the s

Changmao Li, Jeffrey Flanigan

Predicting the future is of great interest across many aspects of human activity. Businesses are interested in future trends, traders are interested in future stock prices, and companies are highly interested in future technological breakthroughs. While there are many automated systems for predicting future numerical data, such as weather, stock prices, and

Chanwook Park, Sourav Saha, Jiachen Guo, Hantao Zhang

Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, applying this transition in engineering software presents challenges, including low surrogate model accuracy, the curse of dimensionality in inverse design, and rising complexity in

Jiawei Sun, Chengran Yuan, Shuo Sun, Shanze Wang

The ability to accurately predict feasible multimodal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Transformer (MTR), a state-of-the-art motion prediction method, alleviated mode collapse and instability during training and enhanced overall prediction performance by replacing conv

Cheol-Hyun Cho, Kyoungmo Kim

A tagged arc on a surface is introduced by Fomin, Shapiro, and Thurston to study cluster theory on marked surfaces. Given a tagged arc system on a graded marked surface, we define its $\mathbb{Z}$-graded $\mathcal{A}_\infty$-category, generalizing the construction of Haiden, Katzarkov, and Kontsevich for arc systems. When a tagged arc system arises from a no

C. Granados, B. Kumar Das, M. F. Ciappina

We investigate the use of light beams carrying orbital angular momentum (OAM) in the context of high harmonic generation (HHG) within semiconductor crystals. Our contribution deals with the transfer and conservation of OAM in the strong-field regime, from the driving laser field to the generated harmonics. To this end, in this work, we combine the semiconduc

Jintao Sun, Hao Fei, Zhedong Zheng, Gangyi Ding

In text-based person search endeavors, data generation has emerged as a prevailing practice, addressing concerns over privacy preservation and the arduous task of manual annotation. Although the number of synthesized data can be infinite in theory, the scientific conundrum persists that how much generated data optimally fuels subsequent model training. We ob

Ossi Kaltiokallio, Elizaveta Rastorgueva-Foi, Jukka Talvitie, Yu Ge

The intrinsic geometric connections between millimeter-wave (mmWave) signals and the propagation environment can be leveraged for simultaneous localization and mapping (SLAM) in 5G and beyond networks. However, estimated channel parameters that are mismatched to the utilized geometric model can cause the SLAM solution to degrade. In this paper, we propose a

Soumen Ghosh, Viktor Vegh, Shahrzad Moinian, Hamed Moradi

Seizure recurrence is an important concern after an initial unprovoked seizure; without drug treatment, it occurs within 2 years in 40-50% of cases. The decision to treat currently relies on predictors of seizure recurrence risk that are inaccurate, resulting in unnecessary, possibly harmful, treatment in some patients and potentially preventable seizures in

Max Kreminski

We diagnose and briefly discuss the dearth of the author: a condition that arises when AI-based creativity support tools for writing allow users to produce large amounts of text without making a commensurate number of creative decisions, resulting in output that is sparse in expressive intent. We argue that the dearth of the author helps to explain a number

Arnab Chaudhuri, Kazunori Kohri

This study undertakes a reconsideration of the potential for a first-order electroweak phase transition, focusing on the next-to-minimal two Higgs doublet model (N2HDM). Our exploration spans diverse parameter spaces associated with the phase transition, with a particular emphasis on examining the generation of stochastic Gravitational Waves (GW) resulting f

Darya Likhareva, Hamsini Sankaran, Sivakumar Thiyagarajan

Researchers must stay current in their fields by regularly reviewing academic literature, a task complicated by the daily publication of thousands of papers. Traditional multi-label text classification methods often ignore semantic relationships and fail to address the inherent class imbalances. This paper introduces a novel approach using the SciBERT model

Piotr Mizerka

The technique of inducing spectral gaps for cohomological Laplacians in degree zero was used by Kaluba, Kielak and Nowak to prove property (T) for $\operatorname{SAut}(F_n)$ and $\operatorname{SL}_n(\mathbb{Z})$. In this paper, we adapt this technique to Laplacians in degree one. This allows to provide a lower bound for the cohomological Laplacian in degree

Fardin Kheirandish, Elmira Bolandhemmat, Narges Cheraghpour, Ronak Moradi

In this letter, we introduce a novel method for investigating dissipation (gain) and thermalization in an open quantum system. In this method, the quantum system is coupled linearly with a copy of itself or with another system described by a finite number of bosonic operators. The time-dependent coupling functions play a fundamental role in this scheme. To d

Xun Li, Guangchen Wang, Yu Wang, Jie Xiong

This paper studies a class of continuous-time linear quadratic (LQ) mean-field game problems. We develop two system transformation data-driven algorithms to approximate the decentralized strategies of the LQ mean-field games. The main feature of the obtained data-driven algorithms is that they eliminate the requirement on all system matrices. First, we trans

Alan Yan

We survey some of the mechanisms used to prove that naturally defined sequences in combinatorics are log-concave. Among these mechanisms are Alexandrov's inequality for mixed discriminants, the Alexandrov Fenchel inequality for mixed volumes, Lorentzian polynomials, and the Hard Lefschetz theorem. We use these mechanisms to prove some new log-concavity and e

Juntaro Wada

We propose a novel leptogenesis scenario in the gauged $U(1)_{L_{\mu}-L_{\tau}}$ model. Achieving successful leptogenesis in the $U(1)_{L_{\mu}-L_{\tau}}$ symmetric phase is challenging due to the absence of a CP phase, caused by restriction from the gauge symmetry. To overcome this issue, we introduce an additional global symmetry, $U(1)_{B-L}$, and a scala

Kyle Hsu, Jubayer Ibn Hamid, Kaylee Burns, Chelsea Finn

Inductive biases are crucial in disentangled representation learning for narrowing down an underspecified solution set. In this work, we consider endowing a neural network autoencoder with three select inductive biases from the literature: data compression into a grid-like latent space via quantization, collective independence amongst latents, and minimal fu

Antonette Shibani, Simon Buckingham Shum

While the excitement around the capabilities of technological advancements is giving rise to new AI-based writing assistants, the overarching ecosystem plays a crucial role in how they are adopted in educational practice. In this paper, we point to key ecological aspects for consideration. We draw insights from extensive research integrated with practice on

Sandeep Sharma

We present a new scheme to create all-optical tunable and lossless waveguide using a controllable coherent Raman process in an atomic rubidium vapor in N-type configuration. We employ a Gaussian Raman field and a Laguerre-Gaussian control field to imprint a high-contrast tunable waveguide-like feature inside the atomic medium. We numerically demonstrate that

Cindy Le, Congrui Hetang, Chendi Lin, Ang Cao

We present EucliDreamer, a simple and effective method to generate textures for 3D models given text prompts and meshes. The texture is parametrized as an implicit function on the 3D surface, which is optimized with the Score Distillation Sampling (SDS) process and differentiable rendering. To generate high-quality textures, we leverage a depth-conditioned S

Sary Drappeau, Igor E. Shparlinski

We obtain a new bound on exponential sums over integers without large prime divisors, improving that of Fouvry and Tenenbaum (1991). For a fixed integer $\nu\ne 0$, we also obtain new bounds on exponential sums with $\nu$-th powers of such integers. The improvement is based on exploiting more precisely the factorisation of integers without large prime diviso

Heng-Yu Chen, Yasuaki Hikida, Yusuke Taki, Takahiro Uetoko

We determine the complex geometries dual to the semi-classical saddles in three-dimensional gravity with positive or negative cosmological constant. We examine the semi-classical saddles in Liouville field theory and interpret them in terms of gravity theory. For this, we describe the gravity theory by Chern Simons theory and classify the possible saddles ba

Han Hong

In this paper, we prove that there are no complete noncompact constant mean curvature hypersurfaces with the mean curvature $H > 1$, finite index and finite topology in hyperbolic space $\mathbb{H}^4$. A more general nonexistence result can be proved in a $4$-dimensional Riemannian manifold with certain curvature conditions. We also show that $4$-manifold wi

Vincent Grari, Marcin Detyniecki

This paper presents a novel approach to optimizing profit margins in non-life insurance markets through a gradient descent-based method, targeting three key objectives: 1) maximizing profit margins, 2) ensuring conversion rates, and 3) enforcing fairness criteria such as demographic parity (DP). Traditional pricing optimization, which heavily lean on linear

Liang Li, Ting Zhou, Tong Liu, Zhiwei Liu

Artificial neuronal devices are the basic building blocks for neuromorphic computing systems, which have been motivated by realistic brain emulation. Aiming for these applications, various device concepts have been proposed to mimic the neuronal dynamics and functions. While till now, the artificial neuron devices with high efficiency, high stability and low

R V Raghavendra Rao, U Srinivasulu Reddy

The challenge of imbalanced soil nutrient datasets significantly hampers accurate predictions of soil fertility. To tackle this, a new method is suggested in this research, combining Uniform Manifold Approximation and Projection (UMAP) with Least Absolute Shrinkage and Selection Operator (LASSO). The main aim is to counter the impact of uneven data distribut

Paul Apisa, David Aulicino

We show that the only algebraically primitive invariant subvarieties of strata of translation surfaces with quadratic field of definition are the decagon, Weierstrass curves, and eigenform loci in genus two and the rank two example in the minimal stratum of genus four translation surfaces discovered by Eskin-McMullen-Mukamel-Wright.

Yoshio Kato, Shuhei Tarashima

Transmittance estimators such as Occupancy Grid (OG) can accelerate the training and rendering of Neural Radiance Field (NeRF) by predicting important samples that contributes much to the generated image. However, OG manages occupied regions in the form of the dense binary grid, in which there are many blocks with the same values that cause redundant examina

Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input

Jeremy J. Williams, Felix Liu, Jordy Trilaksono, David Tskhakaya

As fusion energy devices advance, plasma simulations are crucial for reactor design. Our work extends BIT1 hybrid parallelization by integrating MPI with OpenMP and OpenACC, focusing on asynchronous multi-GPU programming. Results show significant performance gains: 16 MPI ranks plus OpenMP threads reduced runtime by 53% on a petascale EuroHPC supercomputer,

Takashi Kaneko

We review recent progress from lattice QCD for the determination of the Cabibbo-Kobayashi-Maskawa matrix elements.

Yue Zhou, Barbara Di Eugenio, Brian Ziebart, Lisa Sharp

Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues. It is particularly beneficial, however cost-prohibitive, for low-socioeconomic status populations due to its highly personalized and labor-intensive nature. In this paper, we propose a neuro-symbolic g

Jiahao Wang, Caixia Yan, Haonan Lin, Weizhan Zhang

Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject. Existing methods try to tackle this challenge and generate consistent content in various ways. However, they either depend on external restricted data or require expensive tuning o

Yilin Gao, Sai Kumar Arava, Yancheng Li, James W. Snyder

Artificial intelligence (AI) is widely deployed to solve problems related to marketing attribution and budget optimization. However, AI models can be quite complex, and it can be difficult to understand model workings and insights without extensive implementation teams. In principle, recently developed large language models (LLMs), like GPT-4, can be deploye

Sam Jeralds

Let $G$ be a simple, simply-connected complex algebraic group with Lie algebra $\mathfrak{g}$, and $G/B$ the associated complete flag variety. The Hochschild cohomology $HH^\bullet(G/B)$ is a geometric invariant of the flag variety related to its generalized deformation theory and has the structure of a $\mathfrak{g}$-module. We study this invariant via repr

Neptune Baro, Partha Pratim Mondal

We report the realization of the first planar optical tweezer trap system by a sheet of light. To visualize the trapping of the target object (dielectric bead or live cell) in a plane, an orthogonal widefield detection is employed. The planar / two-dimensional lightsheet optical tweezer (2D-LOT) sub-system is realized in an inverted microscopy mode with illu

Dong He, Jie Fan, Xin Gao, Yu Gao

Possible light bosonic dark matter interactions with the Standard Model photon have been searched by microwave resonant cavities. In this paper, we demonstrate the cryogenic readout system calibration of a 7.138 GHz copper cavity with a loaded quality factor $Q_l=10^4$, operated at 22 mK temperature based on a dilution refrigerator. Our readout system consis

Yuning Wang, Zhiyuan Liu, Haotian Lin, Junkai Jiang

Scene understanding, defined as learning, extraction, and representation of interactions among traffic elements, is one of the critical challenges toward high-level autonomous driving (AD). Current scene understanding methods mainly focus on one concrete single task, such as trajectory prediction and risk level evaluation. Although they perform well on speci

Pan Shang, Huangyue Chen, Lingchen Kong

Fused Lasso was proposed to characterize the sparsity of the coefficients and the sparsity of their successive differences for the linear regression. Due to its wide applications, there are many existing algorithms to solve fused Lasso. However, the computation of this model is time-consuming in high-dimensional data sets. To accelerate the calculation of fu

Eduardo Fernandes Montesuma, Fred Ngolè Mboula, Antoine Souloumiac

In this paper, we tackle Multi-Source Domain Adaptation (MSDA), a task in transfer learning where one adapts multiple heterogeneous, labeled source probability measures towards a different, unlabeled target measure. We propose a novel framework for MSDA, based on Optimal Transport (OT) and Gaussian Mixture Models (GMMs). Our framework has two key advantages.

Xiaomin Fang, Jie Gao, Jing Hu, Lihang Liu

While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field. This challenge is particularly pronounced in scenarios involving complexes with protein chains from different species, such as antigen-antibody interactions, where accuracy often falls short. Limi

Tunazzina Islam, Dan Goldwasser

The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussions poses a continual challenge for these techniques due to the constant shifting of the focus. On the other hand, traditional unsupervised m

Mamtaj Akter, Leena Alghamdi, Dylan Gillespie, Nazmus Miazi

Smartphone users install numerous mobile apps that require access to different information from their devices. Much of this information is very sensitive, and users often struggle to manage these accesses due to their lack of tech expertise and knowledge regarding mobile privacy. Thus, they often seek help from others to make decisions regarding their mobile

Kai Huang, Lijun Zhang, Yongzhang Yang, Mao Ye

This paper introduces a novel dynamical model, building upon the existing dynamical model for Deimos in the current numerical ephemerides, which only encompasses the simple libration effects of Deimos. The study comprehensively incorporates the rotational dynamics of Deimos influenced by the torque exerted by the major celestial bodies (Mars, the Sun) in the

Shaocong Liang, Jialin Cheng, Jiliang Qin, Jiatong Li

Quantum dense coding (QDC) means to transmit two classical bits by only transferring one quantum bit, which has enabled high-capacity information transmission and strengthened system security. Continuousvariable QDC offers a promising solution to increase communication rates while achieving seamless integration with classical communication systems. Here, we

Aldo Rodriguez-Puebla

The current cosmological paradigm asserts that dark matter halos provide the gravitational scaffolding for galaxy formation through a combination of hierarchical structure formation and non-linear local (g)astrophysical processes. This close relationship, known as the galaxy-halo connection, suggests that the growth and assembly of dark matter halos impact t

Zhiyuan Wu, Sheng Sun, Yuwei Wang, Min Liu

On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI inference without relying on remote servers. However, training models for on-device deployment face significant challenges due to the decentralized and privacy-sensitive nature of users' data, along with end-side constra

Simon Johnston, Dipanjan Mitra, Michael Keith, Lucy Oswald

The S-shaped swing of the linear polarization position angle (PPA) observed in many pulsars can be interpreted by the rotating vector model (RVM). However, efforts to fit the RVM for a large sample of pulsars observed with the MeerKAT telescope as a part of the Thousand-Pulsar-Array (TPA) programme, only succeeded for about half the cases. High time-resoluti

Xiaohui Duan, Yuxuan Li, Zhao Liu, Bin Yang

With current and future leading systems adopting heterogeneous architectures, adapting existing models for heterogeneous supercomputers is of urgent need for improving model resolution and reducing modeling uncertainty. This paper presents our three-week effort on porting a complex earth system model, CESM 2.2, to a 40-million-core Sunway supercomputer. Taki

Jiyuan Pei, Jialin Liu, Yi Mei

In many practical applications, usually, similar optimisation problems or scenarios repeatedly appear. Learning from previous problem-solving experiences can help adjust algorithm components of meta-heuristics, e.g., adaptively selecting promising search operators, to achieve better optimisation performance. However, those experiences obtained from previousl

Daniel Rudolf, Aaron Smith, Matias Quiroz

This chapter surveys progress on three related topics in perturbations of Markov chains: the motivating question of when and how "perturbed" MCMC chains are developed, the theoretical problem of how perturbation theory can be used to analyze such chains, and finally the question of how the theoretical analyses can lead to practical advice.

Mark Edward M. Gonzales, Hans Oswald A. Ibrahim, Elyssia Barrie H. Ong, Ryan Austin Fernandez

Creative coding is an experimentation-heavy activity that requires translating high-level visual ideas into code. However, most languages and libraries for creative coding may not be adequately intuitive for beginners. In this paper, we present AniFrame, a domain-specific language for drawing and animation. Designed for novice programmers, it (i) features an

Chaoqun Li, Weijie Yang, Hao Liu, Xinyuan Liu

Developing solid-state hydrogen storage materials is as pressing as ever, which requires a comprehensive understanding of the dehydrogenation chemistry of a solid-state hydride. Transition state search and kinetics calculations are essential to understanding and designing high-performance solid-state hydrogen storage materials by filling in the knowledge gap

Feng Lü

In this paper, by making use of properties of elliptic functions, we describe meromorphic solutions of Fermat-type functional equations $f(z)^{n}+f(L(z))^{m}=1$ over the complex plane $\mathbb{C}$, where $L(z)$ is a nonconstant entire function, $m$ and $n$ are two positive integers. As applications, we also consider meromorphic solutions of Fermat-type diffe

Jiehua Mai, Kesong Yan, Fanping Zeng

More than a century ago, L. E. J. Brouwer proved a famous theorem, which says that any orientation preserving homeomorphism of the plane having a periodic point must have a fixed point. In recent years, there are still some authors giving various proofs of this fixed point theorem. In \cite{Fa}, Fathi showed that the condition``having a periodic point'' in t

Peter K. Day, Nicholas F. Cothard, Christopher Albert, Logan Foote

We report measurements characterizing the performance of a kinetic inductance detector array designed for a wavelength of 25 microns and very low optical background level suitable for applications such as a far-infrared instrument on a cryogenically cooled space telescope. In a pulse counting mode of operation at low optical flux, the detectors can resolve i

Zhiyuan Zheng, Yong Shi, Shuowen Jin, H. Dannerbauer

Active galactic nuclei (AGNs) are known to exhibit optical/UV variability and most of them can be well modeled by the damped random walks. Physical processes that are not related to the accretion disk, such as tidal disruption events (TDE) or moving foreground dusty clouds, can cause flare-like and eclipse-like features in the optical light curve. Both long-

Dongyang Liu, Minghui Song, Yuhang Zhang

This paper proposes an adaptive numerical method for stochastic delay differential equations (SDDEs) with a non-global Lipschitz drift term and a non-constant delay, building upon the work of Wei Fang and others. The method adapts the step size based on the growth of the drift term. Differing slightly from the conventional Euler-Maruyama format, this paper a

Zhuoqian Yang, Ke Han, Linwei Liao, Jiaxin Wu

Identifying and managing high-emitters among heavy-duty diesel vehicles is a key to mitigating urban air pollution, as a small number of such vehicles could contribute a significant amount of total transport emissions. On-board monitoring (OBM) systems can directly monitor the real-time emission performance of heavy-duty vehicles on road and have become part

Oren Kraus, Kian Kenyon-Dean, Saber Saberian, Maryam Fallah

Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores the scaling properties of weakly supervised classifiers and self-supervised masked autoencoders (MAEs) when training with increasingly larger model backbones and microscopy datase

Liuyi Wang, Zongtao He, Ronghao Dang, Mengjiao Shen

In the pursuit of robust and generalizable environment perception and language understanding, the ubiquitous challenge of dataset bias continues to plague vision-and-language navigation (VLN) agents, hindering their performance in unseen environments. This paper introduces the generalized cross-modal causal transformer (GOAT), a pioneering solution rooted in

Fan Zhang, Jinfeng Chen, Yu Hu, Zhiqiang Gao

Model uncertainty presents significant challenges in vibration suppression of multi-inertia systems, as these systems often rely on inaccurate nominal mathematical models due to system identification errors or unmodeled dynamics. An observer, such as an extended state observer (ESO), can estimate the discrepancy between the inaccurate nominal model and the t

M. G. González, M. Vera, A. Dreszman, L. J. Rey Vega

In this paper we consider the problem of acoustic inversion in the context of the optoacoustic tomography image reconstruction problem. By leveraging the ability of the recently proposed diffusion models for image generative tasks among others, we devise an image reconstruction architecture based on a conditional diffusion process. The scheme makes use of an

Ryota Nozawa, Shun Sato, Takayasu Matsuo

Nesterov's acceleration in continuous optimization can be understood in a novel way when Nesterov's accelerated gradient (NAG) method is considered as a linear multistep (LM) method for gradient flow. Although the NAG method for strongly convex functions (NAG-sc) has been fully discussed, the NAG method for $L$-smooth convex functions (NAG-c) has not. To fil

Songtao Jiang, Tuo Zheng, Yan Zhang, Yeying Jin

Recent advancements in general-purpose or domain-specific multimodal large language models (LLMs) have witnessed remarkable progress for medical decision-making. However, they are designated for specific classification or generative tasks, and require model training or finetuning on large-scale datasets with sizeable parameters and tremendous computing, hind

Marek Rogatko

Using Ernst formalism, stationary axisymmetric black hole solution in Einstein-dark matter-dark energy gravity has been elaborated. The dark sector was chosen as dark photon concept, where an auxiliary U(1)-gauge field coupled to ordinary Maxwell one was introduced, while dark energy was modelled by the existence of positive cosmological constant. Refining o

Xibin Jin, Guoliang Li, Shuai Wang, Miaowen Wen

Integrated sensing and communication (ISAC) is a promising solution to accelerate edge inference via the dual use of wireless signals. However, this paradigm needs to minimize the inference error and latency under ISAC co-functionality interference, for which the existing ISAC or edge resource allocation algorithms become inefficient, as they ignore the inte

Jixiang Luo

The burgeoning volume of digital content across diverse modalities necessitates efficient storage and retrieval methods. Conventional approaches struggle to cope with the escalating complexity and scale of multimedia data. In this paper, we proposed framework addresses this challenge by fusing AI-native multi-modal search capabilities with neural image compr

Jingran Xu, Huizhi Wang, Yong Zeng, Xiaoli Xu

Integrated super-resolution sensing and communication (ISSAC) is a promising technology to achieve extremely high sensing performance for critical parameters, such as the angles of the wireless channels. In this paper, we propose an ISSAC-based channel estimation method, which requires little or even no pilot, yet still achieves accurate channel state inform

Kai Zheng, Miaowen Wen, Tianqi Mao, Lixia Xiao

The recent proposed affine frequency division multiplexing (AFDM) employing a multi-chirp waveform has shown its reliability and robustness in doubly selective fading channels. In the existing embedded pilot-aided channel estimation methods, the presence of guard symbols in the discrete affine Fourier transform (DAFT) domain causes inevitable degradation of

Harsh Modi, Zhu Chen, Xiao Liang, Minghui Zheng

Iterative learning control (ILC) is a method for reducing system tracking or estimation errors over multiple iterations by using information from past iterations. The disturbance observer (DOB) is used to estimate and mitigate disturbances within the system, while the system is being affected by them. ILC enhances system performance by introducing a feedforw

Hongtai Yang, Luna Liu, Ke Han, Boyi Lei

Fugitive road dust (FRD), as one of the major pollutants in the city, poses great harm to the environment and the physical health of citizens. A common countermeasure adopted by government agencies is employing on-road water trucks (sprinklers) to spray water (sprinkle) on urban streets to reduce the FRD. Currently, the traveling routes of sprinklers are usu

Jiaxuan Wu, Zhengxian Wu, Yiming Xue, Juan Wen

Recent advances in large language models (LLMs) have blurred the boundary of high-quality text generation between humans and machines, which is favorable for generative text steganography. While, current advanced steganographic mapping is not suitable for LLMs since most users are restricted to accessing only the black-box API or user interface of the LLMs,

Joshua Melton, Shannon Reid, Gabriel Terejanu, Siddharth Krishnan

The high volume and rapid evolution of content on social media present major challenges for studying the stance of social media users. In this work, we develop a two stage stance labeling method that utilizes the user-hashtag bipartite graph and the user-user interaction graph. In the first stage, a simple and efficient heuristic for stance labeling uses the

Pengfei Xie, Wenqiang Xu, Tutian Tang, Zhenjun Yu

This work proposes a novel learning framework for visual hand dynamics analysis that takes into account the physiological aspects of hand motion. The existing models, which are simplified joint-actuated systems, often produce unnatural motions. To address this, we integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create a ne

Elham J. Barezi, Parisa Kordjamshidi

We analyze knowledge-based visual question answering, for which given a question, the models need to ground it into the visual modality and retrieve the relevant knowledge from a given large knowledge base (KB) to be able to answer. Our analysis has two folds, one based on designing neural architectures and training them from scratch, and another based on la

Ahmed E. Hassan, Gustavo A. Oliva, Dayi Lin, Boyuan Chen

The advent of Foundation Models (FMs) and AI-powered copilots has transformed the landscape of software development, offering unprecedented code completion capabilities and enhancing developer productivity. However, the current task-driven nature of these copilots falls short in addressing the broader goals and complexities inherent in software engineering (

Sajag Kumar, Sayan Choudhury

Periodically driven classical many-body systems can host a rich zoo of prethermal dynamical phases. In this work, we extend the paradigm of classical prethermalization to aperiodically driven systems. We establish the existence of a long-lived prethermal regime in spin systems subjected to random multipolar drives (RMDs). We demonstrate that the thermalizati

Oskar Leimkuhler, K. Birgitta Whaley

Electronic ground states are of central importance in chemical simulations, but have remained beyond the reach of efficient classical algorithms except in cases of weak electron correlation or one-dimensional spatial geometry. We introduce a hybrid quantum-classical eigenvalue solver that constructs a wavefunction ansatz from a linear combination of matrix p

Rishab Dutta, Nam P. Vu, Chuzhi Xu, Delmar G. A. Cabral

Quantum harmonic oscillators, or qumodes, provide a promising and versatile framework for quantum computing. Unlike qubits, which are limited to two discrete levels, qumodes have an infinite-dimensional Hilbert space, making them well-suited for a wide range of quantum simulations. In this work, we focus on the molecular electronic structure problem. We prop

Peiyuan Zhi, Zhiyuan Zhang, Yu Zhao, Muzhi Han

Autonomous robot navigation and manipulation in open environments require reasoning and replanning with closed-loop feedback. In this work, we present COME-robot, the first closed-loop robotic system utilizing the GPT-4V vision-language foundation model for open-ended reasoning and adaptive planning in real-world scenarios.COME-robot incorporates two key inn